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Yearbook of Medical Informatics Aug 2018To introduce and summarize current research in the field of Public Health and Epidemiology Informatics. (Review)
Review
OBJECTIVES
To introduce and summarize current research in the field of Public Health and Epidemiology Informatics.
METHODS
The 2017 literature concerning public health and epidemiology informatics was searched in PubMed and Web of Science, and the returned references were reviewed by the two section editors to select 14 candidate best papers. These papers were then peer-reviewed by external reviewers to provide the editorial team with an enlightened vision to select the best papers.
RESULTS
Among the 843 references retrieved from PubMed and Web of Science, two were finally selected as best papers. The first one analyzes the relationship between the disease, social/mass media, and public emotions to understand public overreaction (leading to a noticeable reduction of social and economic activities) in the context of a nation-wide outbreak of Middle East Respiratory Syndrome (MERS) in Korea in 2015. The second paper concerns a new methodology to de-identify patient notes in electronic health records based on artificial neural networks that outperformed existing methods.
CONCLUSIONS
Surveillance is still a productive topic in public health informatics but other very important topics in Public Health are appearing. For example, the use of artificial intelligence approaches is increasing.
Topics: Artificial Intelligence; Data Anonymization; Epidemiology; Machine Learning; Neural Networks, Computer; Public Health; Public Health Informatics
PubMed: 30157525
DOI: 10.1055/s-0038-1667082 -
Artificial intelligence and machine learning: the resurgence of the industrial revolution by robots.Open Heart Jan 2022
Topics: Artificial Intelligence; Cardiovascular Diseases; Follow-Up Studies; Humans; Machine Learning; Robotics
PubMed: 35046123
DOI: 10.1136/openhrt-2021-001883 -
The British Journal of Radiology Oct 2023Artificial intelligence has been introduced to clinical practice, especially radiology and radiation oncology, from image segmentation, diagnosis, treatment planning and... (Review)
Review
Artificial intelligence has been introduced to clinical practice, especially radiology and radiation oncology, from image segmentation, diagnosis, treatment planning and prognosis. It is not only crucial to have an accurate artificial intelligence model, but also to understand the internal logic and gain the trust of the experts. This review is intended to provide some insights into core concepts of the interpretability, the state-of-the-art methods for understanding the machine learning models, the evaluation of these methods, identifying some challenges and limits of them, and gives some examples of medical applications.
Topics: Humans; Artificial Intelligence; Radiation Oncology; Radiology; Machine Learning; Radiography
PubMed: 37493248
DOI: 10.1259/bjr.20230142 -
Acta Obstetricia Et Gynecologica... Feb 2024
Topics: Humans; Artificial Intelligence; Machine Learning
PubMed: 38284152
DOI: 10.1111/aogs.14772 -
International Wound Journal Apr 2019
Topics: Artificial Intelligence; Humans; Machine Learning; Wounds and Injuries
PubMed: 30887702
DOI: 10.1111/iwj.13108 -
Indian Journal of Pathology &... May 2022Machine learning and artificial intelligence (AI) have become a part of our daily routine. There are very few of us who are not influenced by this technology. There are... (Review)
Review
Machine learning and artificial intelligence (AI) have become a part of our daily routine. There are very few of us who are not influenced by this technology. There are a lot of misconceptions about the scope, utility, and fallacies of AI. Digital neuropathology is an evolving area of research. The importance of digital image processing stems from the rapid gains in computer vision and image processing that have happened in the past decade thanks to advancements in deep learning (DL). The article attempts to present to the audience a simple presentation of the technology and attempts to provide a context-based understanding of the DL process for image processing. Also highlighted are current challenges and the roadblocks in adopting the technology in routine neuropathology.
Topics: Artificial Intelligence; Humans; Image Processing, Computer-Assisted; Machine Learning; Neural Networks, Computer
PubMed: 35562153
DOI: 10.4103/ijpm.ijpm_115_22 -
International Journal of Molecular... Sep 2022Recent technological innovations in the field of mass spectrometry have supported the use of metabolomics analysis for precision medicine. This growth has been allowed... (Review)
Review
Recent technological innovations in the field of mass spectrometry have supported the use of metabolomics analysis for precision medicine. This growth has been allowed also by the application of algorithms to data analysis, including multivariate and machine learning methods, which are fundamental to managing large number of variables and samples. In the present review, we reported and discussed the application of artificial intelligence (AI) strategies for metabolomics data analysis. Particularly, we focused on widely used non-linear machine learning classifiers, such as ANN, random forest, and support vector machine (SVM) algorithms. A discussion of recent studies and research focused on disease classification, biomarker identification and early diagnosis is presented. Challenges in the implementation of metabolomics-AI systems, limitations thereof and recent tools were also discussed.
Topics: Algorithms; Artificial Intelligence; Machine Learning; Precision Medicine; Support Vector Machine
PubMed: 36232571
DOI: 10.3390/ijms231911269 -
Current Heart Failure Reports Aug 2023The introduction of Artificial Intelligence into the healthcare system offers enormous opportunities for biomedical research, the improvement of patient care, and cost... (Review)
Review
PURPOSE OF REVIEW
The introduction of Artificial Intelligence into the healthcare system offers enormous opportunities for biomedical research, the improvement of patient care, and cost reduction in high-end medicine. Digital concepts and workflows are already playing an increasingly important role in cardiology. The fusion of computer science and medicine offers great transformative potential and enables enormous acceleration processes in cardiovascular medicine.
RECENT FINDINGS
As medical data becomes smart, it is also becoming more valuable and vulnerable to malicious actors. In addition, the gap between what is technically possible and what is allowed by privacy legislation is growing. Principles of the General Data Protection Regulation that have been in force since May 2018, such as transparency, purpose limitation, and data minimization, seem to hinder the development and use of Artificial Intelligence. Concepts to secure data integrity and incorporate legal and ethical principles can help to avoid the potential risks of digitization and may result in an European leadership in regard to privacy protection and AI. The following review provides an overview of relevant aspects of Artificial Intelligence and Machine Learning, highlights selected applications in cardiology, and discusses central ethical and legal considerations.
Topics: Humans; Artificial Intelligence; Heart Failure; Machine Learning; Cardiology; Delivery of Health Care
PubMed: 37291432
DOI: 10.1007/s11897-023-00606-0 -
Journal of Medical Toxicology :... Oct 2020Artificial intelligence (AI) refers to machines or software that process information and interact with the world as understanding beings. Examples of AI in medicine... (Review)
Review
Artificial intelligence (AI) refers to machines or software that process information and interact with the world as understanding beings. Examples of AI in medicine include the automated reading of chest X-rays and the detection of heart dysrhythmias from wearables. A key promise of AI is its potential to apply logical reasoning at the scale of data too vast for the human mind to comprehend. This scaling up of logical reasoning may allow clinicians to bring the entire breadth of current medical knowledge to bear on each patient in real time. It may also unearth otherwise unreachable knowledge in the attempt to integrate knowledge and research across disciplines. In this review, we discuss two complementary aspects of artificial intelligence: deep learning and knowledge representation. Deep learning recognizes and predicts patterns. Knowledge representation structures and interprets those patterns or predictions. We frame this review around how deep learning and knowledge representation might expand the reach of Poison Control Centers and enhance syndromic surveillance from social media.
Topics: Artificial Intelligence; Big Data; Data Mining; Deep Learning; Humans; Knowledge Bases; Markov Chains; Neural Networks, Computer; Psychotropic Drugs; Toxicology; Vocabulary, Controlled
PubMed: 32215849
DOI: 10.1007/s13181-020-00769-5 -
Journal of Medical Internet Research Jul 2019Applications of artificial intelligence (AI) in health care have garnered much attention in recent years, but the implementation issues posed by AI have not been...
BACKGROUND
Applications of artificial intelligence (AI) in health care have garnered much attention in recent years, but the implementation issues posed by AI have not been substantially addressed.
OBJECTIVE
In this paper, we have focused on machine learning (ML) as a form of AI and have provided a framework for thinking about use cases of ML in health care. We have structured our discussion of challenges in the implementation of ML in comparison with other technologies using the framework of Nonadoption, Abandonment, and Challenges to the Scale-Up, Spread, and Sustainability of Health and Care Technologies (NASSS).
METHODS
After providing an overview of AI technology, we describe use cases of ML as falling into the categories of decision support and automation. We suggest these use cases apply to clinical, operational, and epidemiological tasks and that the primary function of ML in health care in the near term will be decision support. We then outline unique implementation issues posed by ML initiatives in the categories addressed by the NASSS framework, specifically including meaningful decision support, explainability, privacy, consent, algorithmic bias, security, scalability, the role of corporations, and the changing nature of health care work.
RESULTS
Ultimately, we suggest that the future of ML in health care remains positive but uncertain, as support from patients, the public, and a wide range of health care stakeholders is necessary to enable its meaningful implementation.
CONCLUSIONS
If the implementation science community is to facilitate the adoption of ML in ways that stand to generate widespread benefits, the issues raised in this paper will require substantial attention in the coming years.
Topics: Artificial Intelligence; Humans; Machine Learning; Telemedicine
PubMed: 31293245
DOI: 10.2196/13659